Inflammation as a cause of malnutrition, atherosclerotic cardiovascular disease, and poor outcome in hemodialysis patients
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the major cause of morbidity and mortality in end-stage renal disease (ESRD) patients treated by hemodialysis (HD). Although traditional risk factors are common in dialysis patients, they may not alone be sufficient to account for the unacceptable high prevalence of CVD in this patient group. Recent evidence demonstrates that chronic inflammation, a nontraditional risk factor that is commonly observed in HD patients, may cause malnutrition and progressive atherosclerotic CVD by several pathogenetic mechanisms. The cause(s) of inflammation in HD patients is multifactorial and includes both dialysis-related (such as graft and fistula infections, bioincompatibility, impure dialysate, and back-filtration) and dialysis-unrelated factors. Although inflammation may reflect underlying CVD, an acute-phase reaction may also be a direct cause of vascular injury. Available data suggest that proinflammatory cytokines play a central role in the genesis of both malnutrition and CVD in ESRD. Thus, it could be speculated that suppression of the vicious cycle of malnutrition, inflammation, and atherosclerosis (MIA syndrome) would improve survival in dialysis patients. As there is not yet any recognized, or even proposed, targeted treatment for ESRD patients with chronic inflammation; it would be of considerable interest to study the long-term effect of various anti-inflammatory treatment strategies on nutritional and cardiovascular status as well as outcome in these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".